In digital image processing it is often useful to find the eye-mouth coordination, that is, to detect/locate an eye and mouth position. This information can be used, for example, to find the pose of a human face in the image. Since human faces may often be distinguished by their features, eye-mouth coordination also can be used as a pre-processor for applications such as face recognition that is further used in image retrieval.
U.S. Pat. No. 6,072,892 (Kim) which issued Jun. 6, 2000 discloses an eye position detecting apparatus and method. The disclosed method for detecting the position of eyes in a facial image uses a thresholding method on an intensity histogram of the image to find three peaks in the histogram representing skin, white of the eye, and pupil.
While this method may have achieved a certain degree of success in its particular application, one of the problems with this method is that it needs to scan the entire image pixel by pixel and position a search window at each pixel. As such, it consumes enormous computing power. Further, it may also produce a high rate of false positives because similar histogram patterns occur in places other than eye regions.
In “Using color and geometric models for extracting facial features”, Journal of Imaging Science and Technology, Vol. 42, No. 6, pp. 554–561, 1998, Tomoyuki Ohtsuki of Sony Corporation proposed a region segmentation method to find mouth candidates. However, a region segmentation, in general, is very sensitive to luminance and chromaticity variations, and therefore very unstable.
Accordingly, a need continues to exist for a method of utilizing information embedded in a digital facial image to determine human eye-mouth coordination in a robust, yet computationally efficient manner.